Lincoff James, Haghighatlari Mojtaba, Krzeminski Mickael, Teixeira João M C, Gomes Gregory-Neal W, Gradinaru Claudiu C, Forman-Kay Julie D, Head-Gordon Teresa
Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720.
Pitzer Center for Theoretical Chemistry, University of California, Berkeley, CA 94720.
Commun Chem. 2020;3. doi: 10.1038/s42004-020-0323-0. Epub 2020 Jun 9.
Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii ( ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.
具有内在无序或未折叠状态无序的蛋白质构成了结构生物学的一个新前沿领域，需要对多样且动态的结构集合进行表征。我们引入了一个全面的贝叶斯框架，即扩展实验推断结构测定（X-EISD）方法，该方法可计算无序蛋白质集合的最大对数似然值。X-EISD考虑了一系列实验数据以及来自结构的反算模型的不确定性，包括核磁共振化学位移、J 耦合、核 Overhauser 效应（NOE）、顺磁弛豫增强（PRE）、残余偶极耦合（RDC）、流体动力学半径（ ）、单分子荧光 Förster 共振能量转移（smFRET）和小角 X 射线散射（SAXS）。我们将 X-EISD 应用于针对未折叠的 drkN SH3 结构域的实验数据进行联合优化，发现将局部数据类型（如化学位移或 J 耦合）与长程约束（如 NOE、PRE 或 smFRET）相结合，如果与代表性的内在无序蛋白质构象体相结合，会产生与所有其他数据类型高度一致的结构集合。
